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Avis et commentaires pour d'étudiants pour Apprentissage mechanique pratique par Université Johns-Hopkins

2,919 évaluations
554 avis

À propos du cours

One of the most common tasks performed by data scientists and data analysts are prediction and machine learning. This course will cover the basic components of building and applying prediction functions with an emphasis on practical applications. The course will provide basic grounding in concepts such as training and tests sets, overfitting, and error rates. The course will also introduce a range of model based and algorithmic machine learning methods including regression, classification trees, Naive Bayes, and random forests. The course will cover the complete process of building prediction functions including data collection, feature creation, algorithms, and evaluation....

Meilleurs avis


Mar 01, 2017

Issues of every stage of the construction of learning machine model, as well as issues with several different machine learning methods are well and in fine yet very understandable detail explained.


Jun 18, 2018

Excellent introduction to basic ML techniques. A lot of material covered in a short period of time! I will definitely seek more advanced training out of the inspiration provided by this class.

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476 - 500 sur 546 Avis pour Apprentissage mechanique pratique

par Kyle H

May 09, 2018

A brisk introduction to some of the basics of Machine Learning. Will leave with an understanding of a few ways to use the caret package.

par Manuel E

Aug 08, 2019

Good course, but either explanations are too fast paced for the level of difficulty, or my neurons have began to decay with age.

par Noelia O F

Jul 19, 2016

Good course for learning the basics of the caret package. However, it is not a good course for learning machine learning.

par Joseph I

Feb 01, 2020

Material was very interesting but was covered at a very high level and a lot of additional learning was required.

par José A G R

Feb 05, 2017

Superfluous but the existence of the package "caret" covers the gap of other libraries like "skilearn" of python


Mar 01, 2017

Instructor rushes the course and does not explain much in the same level of details as respective quiz requires

par Hongzhi Z

Jan 03, 2018

All the formulas and code in slides are too abstract. If can be more charts to interpret that will be better.

par Henrique C A

Oct 14, 2016

Exercises could be more complete, and some are outdated for latest R, giving slightly different results.

par Alex F

Dec 30, 2018

A fine introduction, but there are much more engaging and better quality courses out there...

par Yingnan X

Feb 11, 2016

If you have taken Andrew Ng's machine learning class, it's not necessary to take this one.

par Yohan A H

Sep 06, 2019

I think it was a very fast course and I feel more real examples would have been useful,

par fabio a a l l

Nov 14, 2017

Poor supporting material in a course that tries to cover a lot in a very limited time.

par Rafael d R S

Jul 24, 2018

this course seemed too rushed for me, too little content for such a extense subject

par Raj V J

Jan 24, 2016

more needs to be taught in class. what is taught is not sufficient for quizzes.

par Surjya N P

Jul 03, 2017

Overally course is good. But weekly programming assignments will be great.

par 王也

Dec 18, 2016

Too different for beginners but not deep enough for ones already know R.

par james

Sep 10, 2016

Quizzes are useful exercises but need to do a lot of self studying.

par Philip A

Feb 27, 2017

mentorship was great, but the video lectures were almost useless.

par Christoph G

Dec 04, 2016

The topic is too big, for one course from my point of view.

par Ariel S G

Jun 27, 2017

In my opinion, this course needs a few extra exercises.

par Jorge L

Oct 13, 2016

Fair but assignments are not very well explained

par Bahaa A

Oct 20, 2016

Good enough to open up mind of researcher

par Johnnery A

Mar 20, 2020

I need study more this course

par Sergio R

Sep 20, 2017

I miss Swirl

par Serene S

Apr 29, 2016

too easy